Adaptive compression of stored data

ABSTRACT

Systems, devices and methods for adaptive compression of stored information includes a memory management computing device programmed to monitor a size of a plurality of data structures stored in a data repository. The computing device compares the size of each of a plurality of data structures to a predetermined threshold. When a size of an uncompressed data structure meets the threshold, the memory management computing device calculates a value of a first compression parameter based on a value of a first parameter and a value of a second parameter of each data element of the uncompressed data structure, calculates a value of a second compression parameter based the value of the first parameter of each data element of the uncompressed data structure, generates a compressed data structure based on the value of the first compression parameter and the second compression parameter; and replaces, in the data repository, the uncompressed data structure with the compressed data structure.

REFERENCE TO RELATED APPLICATIONS

This application is a continuation under 37 C.F.R. § 1.53(b) of U.S.patent application Ser. No. 17/536,886 filed Nov. 29, 2021 now U.S. Pat.No. ______, which is a continuation under 37 C.F.R. § 1.53(b) of U.S.patent application Ser. No. 17/207,881 filed Mar. 22, 2021 now U.S. Pat.No. 11,218,560, which is a continuation under 37 C.F.R. § 1.53(b) ofU.S. patent application Ser. No. 16/792,973 filed Feb. 18, 2020 now U.S.Pat. No. 10,992,766, which is a continuation under 37 C.F.R. § 1.53(b)of U.S. patent application Ser. No. 15/832,244 filed Dec. 5, 2017 nowU.S. Pat. No. 10,609,172, which claims the benefit of the filing dateunder 35 U.S.C. § 119(e) U.S. Provisional Patent Application Ser. No.62/491,040, filed Apr. 27, 2017, the entire disclosures of which ishereby incorporated by reference.

BACKGROUND

While costs associated with computers and memory storage products havebeen falling, with technological improvements, available computingresources of organizations remain at a premium. For example, asbusinesses increasingly move toward electronic communications,electronic processing of business processes, and electronicallymonitoring these communications and business processes, memory usage andcomputing processing power needs increase accordingly. In many cases,computing centers tasked with implementing and maintaining the discussedelectronic communications and business processes are constrained byexisting or aging hardware and software resources, budgetary concernsregarding the purchase, upgrade, or repair of the hardware and softwareinfrastructure components. This may be true for large or small businessorganizations. In an illustrative example, a large organization may havemany clients engaging in large numbers of electronic transactions, thedetails of which may be stored in memory. In many cases, theseelectronic transactions may occur continually and/or concurrently withelectronic transactions with multiple other clients. As such, memorystorage requirements may increase to a predefined limit, such thatcomputing resources may be depleted before additional resources may beadded to the system. Additionally, the data stored may be communicatedbetween computing systems for processing. These communicationrequirements may result in slowed communications capability, ascommunication bandwidth on an organizations network may be finite,regardless of how much data must be communicated. As such, a need hasbeen recognized for improved data management capabilities, in storagecapacity and transmission bandwidth management, while maintainingdesired parameters of the underlying data.

SUMMARY OF THE INVENTION

Systems and methods are described for reducing memory storage andcommunication bandwidth requirements by compressing data based on theunderlying stored information. A memory management computing device maybe programmed to monitor a size of a plurality of data structures storedin a data repository. The computing device may compare the size of eachof a plurality of data structures to a predetermined threshold. When asize of an uncompressed data structure meets the threshold, the memorymanagement computing device may calculate a value of a first compressionparameter based on a value of a first parameter and a value of a secondparameter of each data element of the uncompressed data structure,calculate a value of a second compression parameter based the value ofthe first parameter of each data element of the uncompressed datastructure, and generate a compressed data structure based on the valueof the first compression parameter and the second compression parameter.The memory management computing device may replace, in the datarepository, the uncompressed data structure with the compressed datastructure.

In an illustrative example, a data repository associated with afinancial institution may store thousands of data structures, eachhaving up to ten thousand data elements, where each data structurestores electronic information associated with a different portfolio andeach line item of a particular data structure may correspond to anelectronic transaction associated with the portfolio. In some cases, thefinancial institution may be required to store a record of eachelectronic transaction to meet one or more regulatory requirements. Assuch, as the number of electronic transactions increases, particularlyfor institutional trading firms, the number of records to be stored mayexceed an allowable memory allocation size in a relatively short time.In some cases, the electronic data structures corresponding toelectronic portfolios may be synchronized over a network between two ormore computing systems one or more times a day. In some cases,synchronization may be performed on a real-time (or near real-time)basis. In such cases, synchronizing thousands of portfolios, eachincluding thousands of transactions, may cause communication traffic toslow, particularly during periods of high communication traffic. Byactively monitoring a size of each portfolio data structure, memoryusage may be proactively managed to minimize memory usage requirementsand limit manpower and equipment costs in installing, testing, andactivating additional storage devices. Similarly, communications delaysmay be minimized through actively managing the size of the datastructures communicated and/or synchronized between different computingsystems. Through active management of data structure size, communicationtimes and/or delays may be minimized proportionally.

The details of these and other embodiments of the present disclosure areset forth in the accompanying drawings and the description below. Otherfeatures and advantages of the invention will be apparent from thedescription and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may take physical form in certain parts and steps,embodiments of which will be described in detail in the followingdescription and illustrated in the accompanying drawings that form apart hereof, wherein:

FIG. 1 shows an illustrative trading network environment forimplementing trading systems and methods according to aspects of thedisclosure;

FIG. 2 shows a portion of an illustrative system for adaptivecompression of stored data according to aspects of the disclosure;

FIGS. 3 and 4 show illustrative data tables corresponding to a methodfor adaptive compression of stored data according to aspects of thedisclosure; and

FIG. 5 shows a portion of an illustrative system for adaptivecompression of stored data according to aspects of the disclosure.

DETAILED DESCRIPTION

Illustrative Operating Environment

Aspects of at least some embodiments can be implemented with computersystems and computer networks that allow users to communicate tradinginformation. An exemplary trading network environment for implementingtrading systems and methods according to at least some embodiments isshown in FIG. 1. The implemented trading systems and methods can includesystems and methods, such as are described herein, that facilitatetrading and other activities associated with financial products based oncurrency pairs.

Computer system 100 can be operated by a financial product exchange andconfigured to perform operations of the exchange for, e.g., trading andotherwise processing various financial products. Financial products ofthe exchange may include, without limitation, futures contracts, optionson futures contracts (“futures contract options”), and other types ofderivative contracts. Financial products traded or otherwise processedby the exchange may also include over-the-counter (OTC) products such asOTC forwards, OTC options, etc.

Computer system 100 receives orders for financial products, matchesorders to execute trades, transmits market data related to orders andtrades to users, and performs other operations associated with afinancial product exchange. Exchange computer system 100 may beimplemented with one or more mainframe, desktop or other computers. Inone embodiment, a computer device uses one or more 64-bit processors. Auser database 102 includes information identifying traders and otherusers of exchange computer system 100. Data may include user names andpasswords. An account data module 104 may process account informationthat may be used during trades. A match engine module 106 is included tomatch prices and other parameters of bid and offer orders. Match enginemodule 106 may be implemented with software that executes one or morealgorithms for matching bids and offers.

A trade database 108 may be included to store information identifyingtrades and descriptions of trades. In particular, a trade database maystore information identifying the time that a trade took place and thecontract price. An order book module 110 may be included to store pricesand other data for bid and offer orders, and/or to compute (or otherwisedetermine) current bid and offer prices. A market data module 112 may beincluded to collect market data, e.g., data regarding current bids andoffers for futures contracts, futures contract options and otherderivative products. Module 112 may also prepare the collected marketdata for transmission to users. A risk management module 134 may beincluded to compute and determine a user's risk utilization in relationto the user's defined risk thresholds. An order processor module 136 maybe included to decompose delta based and bulk order types for furtherprocessing by order book module 110 and match engine module 106.

A clearinghouse module 140 may be included as part of exchange computersystem 100 and configured to carry out clearinghouse operations. Module140 may receive data from and/or transmit data to trade database 108and/or other modules of computer system 100 regarding trades of futurescontracts, futures contracts options, OTC options and contracts, andother financial products. Clearinghouse module 140 may facilitate thefinancial product exchange acting as one of the parties to every tradedcontract or other product. For example, computer system 100 may match anoffer by party A to sell a financial product with a bid by party B topurchase a like financial product. Module 140 may then create afinancial product between party A and the exchange and an offsettingsecond financial product between the exchange and party B. As anotherexample, module 140 may maintain margin data with regard to clearingmembers and/or trading customers. As part of such margin-relatedoperations, module 140 may store and maintain data regarding the valuesof various contracts and other instruments, determine mark-to-market andfinal settlement amounts, confirm receipt and/or payment of amounts duefrom margin accounts, confirm satisfaction of final settlementobligations (physical or cash), etc. As discussed in further detailbelow, module 140 may determine values for performance bonds associatedwith trading in products based on various types of currency pairs.

Each of modules 102 through 140 could be separate software componentsexecuting within a single computer, separate hardware components (e.g.,dedicated hardware devices) in a single computer, separate computers ina networked computer system, or any combination thereof (e.g., differentcomputers in a networked system may execute software modulescorresponding more than one of modules 102-140).

Computer device 114 is shown directly connected to exchange computersystem 100. Exchange computer system 100 and computer device 114 may beconnected via a T1 line, a common local area network (LAN) or othermechanism for connecting computer devices. Computer device 114 is shownconnected to a radio 132. The user of radio 132 may be a trader orexchange employee. The radio user may transmit orders or otherinformation to a user of computer device 114. The user of computerdevice 114 may then transmit the trade or other information to exchangecomputer system 100.

Computer devices 116 and 118 are coupled to a LAN 124. LAN 124 mayimplement one or more of the well-known LAN topologies and may use avariety of different protocols, such as Ethernet. Computers 116 and 118may communicate with each other and other computers and devicesconnected to LAN 124. Computers and other devices may be connected toLAN 124 via twisted pair wires, coaxial cable, fiber optics, radio linksor other media.

A wireless personal digital assistant device (PDA) 122 may communicatewith LAN 124 or the Internet 126 via radio waves. PDA 122 may alsocommunicate with exchange computer system 100 via a conventionalwireless hub 128. As used herein, a PDA includes mobile telephones andother wireless devices that communicate with a network via radio waves.

FIG. 1 also shows LAN 124 connected to the Internet 126. LAN 124 mayinclude a router to connect LAN 124 to the Internet 126. Computer device120 is shown connected directly to the Internet 126. The connection maybe via a modem, DSL line, satellite dish or any other device forconnecting a computer device to the Internet. Computers 116, 118 and 120may communicate with each other via the Internet 126 and/or LAN 124.

One or more market makers 130 may maintain a market by providingconstant bid and offer prices for a derivative or security to exchangecomputer system 100. Exchange computer system 100 may also include tradeengine 138. Trade engine 138 may, e.g., receive incoming communicationsfrom various channel partners and route those communications to one ormore other modules of exchange computer system 100.

One skilled in the art will appreciate that numerous additionalcomputers and systems may be coupled to exchange computer system 100.Such computers and systems may include, without limitation, additionalclearing systems (e.g., computer systems of clearing member firms),regulatory systems and fee systems.

The operations of computer devices and systems shown in FIG. 1 may becontrolled by computer-executable instructions stored on non-transitorycomputer-readable media. For example, computer device 116 may includecomputer-executable instructions for receiving market data from exchangecomputer system 100 and displaying that information to a user. Asanother example, clearinghouse module 140 and/or other modules ofexchange computer system 100 may include computer-executableinstructions for performing operations associated with determiningperformance bond contributions associated with holdings in products thatare based on various types of currency pairs.

Of course, numerous additional servers, computers, handheld devices,personal digital assistants, telephones and other devices may also beconnected to exchange computer system 100. Moreover, one skilled in theart will appreciate that the topology shown in FIG. 1 is merely anexample and that the components shown in FIG. 1 may be connected bynumerous alternative topologies.

FIG. 2 shows a portion of an illustrative system 200 for adaptivecompression of stored data according to aspects of the disclosure. Thesystem 200 includes a data repository computing device 220communicatively coupled via a network 205 to one or more computingsystems, such as a remote computing system 240. The data repositorycomputing device 220 may include a non-transitory memory device storinga plurality of data structures 222, 230, and 235. In some cases, a datastructure may include a plurality of data elements (e.g., line items)232. Each line item may include a plurality of parameters 233.

In some cases, the remote computer system 240 may include one or more ofa data repository 242, a computer device 244 and/or a user interface246. The remote computer system 240 may be communicatively coupled to atleast one additional computer system via the network 505. In some cases,the remote computer system 240 may be configured to obtain informationabout one or more of the data structures 222, process the information tocompress a plurality of data elements 232 of the data structures 222,230 and communicate information about the compressed data structures(e.g., data structure 235 to remote computing systems to reduce memoryrequirements associated with storing the data structures 222, 230, and235 while additionally reducing communication times and/orcommunications delays associated with communicating such information. Insome cases, the data repository computing device 220 and/or the remotecomputer system 240 may be communicatively coupled to one or moreadditional computing systems (not shown). In some cases, the one or moreadditional computing systems may communicate (e.g., synchronize) datastructure information to and/or from the data repository computingdevice 220, the remote computer system 240 or both.

In some cases, the remote computing system 240 may monitor a size ofeach of the plurality of data structures 222 and 230, where the size ofthe data structure may correspond to the number of data elements of eachdata structure (e.g., the number of data elements 232 of data structure230). In some cases, the remote computing system may compare the size ofeach data structure 222 and 230 to a pre-defined threshold. In somecases, the size of each data structure may be compared to a samethreshold or each data structure may be assigned a different thresholdbased on characteristics of a user, business organization, or datastored in the data structure. For example, the threshold may be 100 dataelements, 1,000 data elements, 10,000 data elements or the like. In manycases, the remote computing system may be used to manage memoryrequirements for a large number of data structures stored on one or moredifferent computing devices.

In some cases, the remote computing system 240 may process a rule set toperform a data compression algorithm based on information stored in thedata structure to be compressed. For example, one or more parameters ofeach data elements 232 may be used in a data compression algorithm suchthat a plurality of data elements may be compressed to fewer dataelements (e.g., 0, 1, 2, etc.). In a first step of an illustrativecompression algorithm, a value of a first parameter may be used tocalculate a first weighting parameter for each data element, where theweighting parameter may be used to compute a weighted value of a secondparameter for each data element in a second step. In a third step, aweighted compression parameter may be computed, such as a sum of theweighted value of the second parameter for each of the plurality of dataelements of the data structure. In a fourth step, a second compressionparameter may be calculated, such as a sum of the unweighted secondparameter. In a fifth step, a first data element of a compressed datastructure (e.g., 235) may be generated based on information of theoriginal data elements 232 of the uncompressed data structure (e.g.,data structure 232). For example, values of each third parameter foreach of the data elements 232 may be compared to determine the maximumvalue and the minimum value. This value may be assigned to theassociated parameter of the first data element of the compressed datastructure 235 and a second parameter may be calculated as a function ofthe fist compression parameter and the second parameter. In a sixthstep, a second compressed data element may be generated using the lowestparameter and a difference between the second compression value and thefirst compression value. Once generated, the compressed data element 235may be stored in the data repository 220 to replace the uncompresseddata structure 230 and the uncompressed data structure 230 may bedeleted.

Illustrative Embodiments

In some cases, the clearinghouse module 140 may be configured to monitorand/or otherwise manage a capital obligation associated with a pluralityof swaps, such as a swap portfolio such as by using the processdiscussed above with respect to FIG. 2. In at least some embodiments,the exchange computer system 100 (or “system 100”) receives, stores,generates and/or otherwise and processes data. In accordance withvarious aspects of the invention, a clearinghouse (e.g., theclearinghouse module 140) may act as a guarantor of the agreement forthe derivative. As discussed above, a derivative (e.g., an over thecounter swap) may be cleared and guaranteed by the clearinghouse. Thismay promise more interesting capital efficiencies to allow institutionsto reduce a capital charge associated with a plurality of swaps, such asby reducing a gross notional and/or reducing line items associated withthe plurality of swaps. In addition, reduction of the number of lineitems stored in each data structure in the data repository, memory usagemay be reduced, along with the associated costs. Communication time anddelay requirements may be reduced as well, because fewer and/or smallerdata structures may be communicated over the network.

A data repository associated with a financial institution may storethousands of data structures, each having up to ten thousand dataelements, each data element corresponding to a financial transaction(e.g., an interest rate swap, an interest rate swap in a foreigncurrency, and the like). Memory requirements for the financialinstitution may be reduced by reducing notional amount and/or clearingline items associated with portfolios of swaps that are on anorganization's books. In some embodiments, a computer system may accessdata corresponding to a portfolio that comprises m interest rate swaps,wherein m is an integer greater than one. A financial institutionmanaging the portfolio may have a first data storage capacity largeenough to store information associated with the portfolio comprising minterest rate swaps. The accessed data may comprise multiple datacomponents for each of the interest rate swaps, such as a notional valueand a fixed rate value. Each of the interest rate swaps may correspondto a common tenor. The computer system may calculate parameters for acompressed swap having a risk value equivalent to a sum of risk valuesof the interest rate swaps. The parameters may include a compressed swapnotional value, a compressed swap fixed rate value, and a compressedswap floating rate spread value. The computer system may optionallydetermine, based at least on part on the compressed swap parameters, aperformance bond requirement attributable to the interest rate swaps.The computer system may compare a determined performance bondrequirement to account data associated with a holder of the portfolioand may perform one or more additional actions based on the comparing.

A financial institution associated with the portfolio may have one ormore computing systems (e.g., servers, data repositories, processors,etc.) that may be used, at least in part, to store or otherwise manageportfolios of the financial institution's clients. These financialinstitution computing systems may be sized to manage a specified amountof data associated with aspects of the financial institution's business.This may include managing and/or processing information associated withthe portfolios. As portfolios become larger for one or more of thefinancial institution's clients, the data storage capacity and/orprocessing power necessary to process and/or store this information mayapproach a storage capacity and/or processing power limit of thecurrently installed hardware. As such, the financial institution may berequired to install more computing devices and/or upgrade existingcomputing components to handle the additional information storage and/orprocessing requirements. By monitoring, or otherwise managing the sizeof one or more portfolios, the financial institution may proactivelymanage the computing requirements and the associated costs. For example,the financial institution may monitor a size of a client's portfolio. Ifthe portfolio size approaches a threshold, the financial institutioncomputing system may automatically initiate a portfolio compressionprocess. In other cases, the financial institution computing system mayprovide an indication to an individual, such as a network manager, thatthe computing system is approaching the limits to allow manualinitiation of a portfolio compression process.

In some cases, a method for reducing a notional amount and/or clearingline items associated with a portfolio of swaps may include determiningat least a first fixed rate for use in blending a plurality of swaps.Each of the swaps may have matching economics and a different associatedfixed rate. In some cases, the swaps may include non-matched data, suchas different rates associated with different data elements. The methodmay further include determining, by one or more computing devices, afirst remnant swap using the first fixed rate and determining a secondremnant swap using a second fixed rate, wherein the second fixed rate isdifferent from the first fixed rate.

In some cases, a non-transitory computer-readable medium may containcomputer-executable instructions, that when executed by a processor,cause one or more computing devices to determine a first blend rate foruse in blending a plurality of swaps. Each of the plurality of swaps mayhave matching economics and a different associated fixed rate. Theinstructions may further cause the one or more computing devices todetermine a first remnant swap using the first blend rate and todetermine a second remnant swap using the second blend rate to blend theplurality of swaps.

In some cases, a system for reducing notional amount and/or clearingline items associated with swaps that are on an organization's books mayinclude a network and one or more computing devices. The computingdevices may include a processor and one or more non-transitory memorydevices storing instructions that, when executed by the processor, causethe one or more computing devices to determine a first blend rate and asecond blend rate for use in blending a plurality of swaps. Each of theplurality of swaps may have matching economics and a differentassociated fixed rate. The instructions may further cause the one ormore computing devices to determine a first remnant swap using the firstblend rate and determine a second remnant swap using the second blendrate to blend the plurality of swaps together with first remnant swap.The one or more computing devices may then communicate, via the network,information corresponding to the first remnant swap and the secondremnant swap to an institution associated with the plurality of swaps.

In some cases, a method for compressing a portfolio of swaps may beimplemented by a computer device and include determining a fixed ratefor use in blending a plurality of swaps, where each of the plurality ofswaps may have matching economics and a different associated fixed rate.A computing device may further determine a blended swap for blending theplurality of swaps using the fixed rate, wherein a gross notional of theblended swap could be less than the gross notional amount for theplurality of swaps and/or reducing the number of clearing line items.

In some cases, stored data may include information corresponding totrading of one or more financial products. For example, over-the-counter(OTC) products include financial instruments that are bought, sold,traded, exchanged, and/or swapped between counterparties. Many OTCderivatives exist to fill a wide range of needs for counterparties,including limiting or mitigating exposure to risks and/or maximizingcash flow. After an exchange of an OTC product, counterparties mayexpend resources managing the product for the duration of its life.Management may be complicated based on the number of exchanges and/orthe specific terms of the contract.

An interest rate swap (IRS) is an example of a type of OTC product wherethe parties agree to exchange streams of future interest payments basedon a specified principal or notional amount. Each stream may be referredto as a leg. Swaps are often used to hedge certain risks, for instance,interest rate risk. They can also be used for speculative purposes.

An example of a swap includes a plain fixed-to-floating, or “vanilla,”interest rate swap. The vanilla swap includes an exchange of intereststreams where one stream is based on a floating rate and the otherinterest stream is based on a fixed rate. In a vanilla swap, one partymakes periodic interest payments to the other based on a fixed interestrate. In return for the stream of payments based on the fixed rate, theparty may receive periodic interest payments based on a variable rate.The payments are calculated over the notional amount.

The variable rate may be linked to a periodically known or agreed uponrate for the term of the swap such as the London Interbank Offered Rate(LIBOR). This rate is called variable, because it is reset at thebeginning of each interest calculation period to the then currentreference rate, such as LIBOR published rate. The parties to an IRS swapgenerally utilize these exchanges to limit, or manage, exposure tofluctuations in interest rates, or to obtain lower interest rates thanwould otherwise be unobtainable.

Usually, at least one of the legs to a swap has a variable rate. Thevariable rate may be based on any agreed upon factors such as areference rate, the total return of a swap, an economic statistic, etc.Other examples of swaps include total return swaps, and Equity Swaps.

The expiration or maturity of the future streams of payments may occurwell into the future. Each party may have a book of existing and newIRSs having a variety of maturity dates. The parties may expendsubstantial resources tracking and managing their book of IRSs and otherOTC products. In addition, for each IRS, the party maintains an elementof risk that one of its counterparties will default on a payment.

Currently, financial institutions such as banks trade interest ratepayments and/or interest rate swaps over the counter. Streams of futurepayments must be valued to determine a current market price. The marketvalue of a swap is the sum of the difference between the present valueof the future fixed cash flows and the floating rate and the price ofthe swap is determined based on the fixed rate. Because the fixed rateof a particular swap is determined based on the available fixed rate atthe time the price is struck, the fixed rates associated with twodifferent swaps will rarely be the same. As such, each swap that isstruck causes a separate line item to be booked until an opposite swapwith the same fixed rate is struck. As such, it would be desirable toprovide a way to blend coupons for reducing notional amounts and/or lineitems (e.g., swaps) on a financial organization's books.

In some cases, clients may desire to enter into one or more swaps (e.g.,interest rate swaps) for hedging a position in a market. For example, anorganization may have multiple positions in fixed rate mortgages, whilehaving less exposure to products associated with a floating rate. Atsuch times, the organization may desire to enter into one or more swapswith another party to hedge risks that may be associated with having amajority of fixed rate products. For example, when interest rates fall,the organization may make money by having a majority of fixed rateproducts in a portfolio. However, when the market goes up (e.g.,interest rates rise), the organization may lose the opportunity toprofit from the higher interest rates. By hedging these risks, theparties to the interest rate swaps may have a goal to allow their assetsand/or liabilities to at least remain near the starting levels and/orminimize any losses. Generally, an available fixed rate dictates theprice of a swap, where the fixed rate available at the market changesover time. For example, a dealer may quote a swap at a first rate at atime 0. A short time later (e.g., about 10 minutes, about 30 minutes,etc.), the same dealer may provide a quote for a similar swap, buthaving a second rate that is different from the first rate. Once theswaps are entered, the fixed rate will remain fixed for the lifetime ofthe swap. Over time, a swap purchaser (e.g., an individual, anorganization, a business, etc.) may develop a portfolio of swaps,including the swaps of at least one payer swap (e.g., providing thefixed rate leg of the swap) and/or at least one receiver swap (e.g.,providing the floating rate leg of the swap). Few, if any, swaps mayhave the same interest rate resulting in a large number of swaps toremain open on the organization's books.

An organization or an individual may enter into multiple swaps during agiven period (e.g., a day, a week, a month, etc.) and, as a result, mayhave multiple line items in their books in relation to these swaps. Forexample, a customer may have a first swap for paying a set amount (e.g.,$100 million) and a second swap for receiving the same set amount (e.g.,$100 million). Although these swaps are associated with the samenotional amount, the interest rates are likely to be different. As such,these swaps will not net out. Rather, the $200 million remains open onthe organization's books. These swaps may further be subject toregulatory requirements, such as governmental requirements,international banking requirements (e.g., BASEL 3 requirements), and/orthe like. These regulatory requirements may subject the organization tocapital charges (e.g., a specified cash reserve) to ensure that afinancial organization has enough cash to cover their liabilitiesregarding their swap portfolio.

In an illustrative example, a financial institution may have a houseaccount having a number of swaps open in the account. Under theregulatory requirements, the financial institution is required to setaside capital (e.g., a margin account) to cover the open swaps. Thiscash requirement may be dependent upon, at least in part, on the grossnotional amount and/or the total clearing line items associated with theswap portfolio. As such, the financial organization can reduce itscapital requirements by reducing the number of line items on theirbooks, and/or by reducing the gross notional of the swap portfolio.

In some cases, multiple line items having the same interest rate may becollapsed together (e.g., canceled). For example, a pay swap having anassociated first notional amount of may be offset by a second notionalamount associated with a receive swap when the pay and receive swapshave the same interest rate. However, this is rare. For example, a swapparticipant may use an investment strategy for achieving the same fixedrate for two or more different swaps. In such cases, the customer mayspecify a desired rate for a swap when contacting a dealer. While thedealer may be able to find a counter-party willing enter into a swap atthat rate, the swap may incur a fee to equalize the economics of theswap. For example, at the desired fixed rate, the economics of the swapmay favor the paying party or the receiving party. By equalizing thesedifferences, the swap may then be structured to allow the total value ofthe fixed rate leg to be equal to the floating rate leg of the swap. Ingeneral, when the interest rates are determined for the swaps, theprecision may be specified by one or more parties to the swap. In somecases, the precision of the rates may be limited to a defined precisioncommon to the market, such as about 2 decimal places, about 5 decimalplaces, up to 7 decimal places. In other cases, the rate precision maybe specified to be a precision greater than 7 decimal places, such as 11decimal places, up to 16 decimal places, etc.)

In some cases, a clearinghouse may monitor a portfolio of swaps todetermine whether any of the total notional value of the swap portfoliomay be canceled or otherwise offset. For example, the clearing housemay, on a periodic (e.g., daily) basis, process an algorithm todetermine a net value of a client's swap portfolio and send a message tothe client to terminate a line item, or offset at least a portion of thegross notional value when two or more line items may be collapsed.

In the past, the over-the-counter swap market was largely a bespokemarket, where a customer desiring to enter into a swap would contact,such as by telephone, one or more dealers to determine which dealerwould offer the best price to enter into the deal. In such cases, theswap may be entered on a common platform, but the trade execution wascompleted by phone. Because swaps may not be fully transparent,governmental regulations have required that swaps be executed via a SwapExecution Facility (SEF). A SEF is a regulated platform for swap tradingthat provides pre-trade information, such as bids, offers, and the like,and/or an execution mechanism to facilitate execution of swaptransactions among eligible participants. Over time more and more typesof swaps may be executed via a SEF, such as interest rate swaps. Becausethe SEF may operate using a more automated swap market mechanism, thelikelihood that a customer may enter into different swaps, where eachshare a same interest rate will become increasingly rare. A SEF mayexecute many swaps with multiple coupons at a centralized location. Insome cases, different swaps may share the same, or similar, economics toanother swap. However, the coupons are likely to differ due to the swapsexecuting at different times. As such, a client may quickly build a book(e.g., swap portfolio) with many swap line items, which, in turn, wouldrequire the client to incur a large capital obligation corresponding tothe gross notional and/or the total clearing line items of the book ofswaps.

FIGS. 3 and 4 show illustrative data tables corresponding to a methodfor adaptive compression of stored data according to aspects of thedisclosure. FIG. 3 shows an illustrative chart showing operationsperformed by computer system 100 and/or the computer system 200 and/or500. According to some examples, adaptive compression of stored datawith calculating parameters such as for a hypothetical condensed swapportfolio may be performed based on the illustrative steps of FIG. 3. Insome cases, the following description of some embodiments refers tooperations performed by clearinghouse module 140. In other embodiments,however, some or all of these operations may be performed by othermodules of computer system 100 and/or by modules of one or more othercomputer systems.

Currently, the unilateral “risk-free” compression of stored data, suchas those comprising Brazilian Real (BRL) CDI interest rate swaps (BRLswaps) is limited to standard netting, where the fixed rate on tradeswithin a compression group must match. This problem will limit the lineitem and gross notional reduction that is possible, therefore notallowing for maximum compression of the stored data. The currentdisclosure discusses this problem, allowing the stored data to beadaptively compressed such as by compressing BRL swaps with differingfixed rates, increasing an amount of stored data (e.g. datacorresponding to electronic trades) eligible for compression and,therefore, maximizing the reduction of consumed memory usage and/orallowing for more efficient use of bandwidth in communicatinginformation about the stored data.

While some memory compression algorithms may utilize information storedin the data structures to be compressed, additional parameters and/orother requirements may be required. For example, other BRL swapcompression solutions may either a) require the fixed rate to match oneligible trades and/or b) are multilateral solutions requiring theportfolios of 2+ trading participants to achieve reduction in line itemsor gross notional.

For stored data such as a portfolio of BRL Swaps, adaptive datacompression algorithms, such as coupon blending, can be achieved byfollowing the steps outlined below. The original stored data (e.g., theportfolio 222) will be reduced to a smaller stored data structurecomprising two remnant trades, where the gross notional will also bereduced. FIGS. 3 and 4 show illustrative examples of a computing systemprocessing a set of rules to perform compression of a data structurestoring multiple line items, here shown as a six-step process.

Rule Set

Step 1: Calculate the EXP Rate for each trade within the portfolio.

EXP Rate=(1+r){circumflex over ( )}n/252−1

Where:

EXP Rate=exponential weighting

r=fixed rate

n=Brazil business days in the accrual period

Step 2: Calculate the Weighted Notional (WN) for each trade within theportfolio based on a present value (PV) notional.

WN=PV Notional*EXP Rate

Where:

PV Notional (PVN)=Present Value Notional

Step 3: Calculate the Net Weighted Notional (NWN) of the portfolio

NWN=Σ(WN)

Step 4: Calculate the Net PV Notional (NPN)

NPN=Σ(PVN)

Step 5: Create BRL Swap Remnant 1 by assigning it the highest fixed rateof the original portfolio and calculating the PV Notional of Remnant 1(PVR1).

PVR1=(NWN−NPN*e_min)/(e_max−e_min)

Where:

e_min=EXP rate of lowest fixed rate

e_max=EXP rate of highest fixed rate

Step 6: Create BRL Swap Remnant 2 by assigning it the lowest fixed rateof the original portfolio and calculating the PV Notional of Remnant 2(PVR2).

PVR2=NPN−PVR1

BRL Swap coupon blending reduces operational risk and process times bydecreasing the number of line items (trades) that a system must process,maintain and report without changing the cash flows of the originalportfolio. The illustrative process may be used to compress the datastructures to minimize memory usage associated with a plurality ofstored portfolios. The described algorithm may also reduce the grossnotional outstanding of eligible trades resulting in a decrease ofcapital that a Clearing Member Firm must hold against their cleared swapportfolio.

FIG. 5 shows a portion of an illustrative system 500 for adaptivecompression of stored data, such as blending coupons associated with aplurality of swaps. In some cases, the illustrative system 500 mayinclude a financial institution computing system 510 communicativelycoupled to a clearinghouse computer system 540 via a network 505 (e.g.,a wide area network (WAN), the LAN 124, the Internet 126, etc.). Thefinancial institution computing system 510 may include a data repository512, one or more computing devices 514, and, in some cases, at least oneuser interface 516. In some cases, the data repository 512 may storeinformation about one or more swap portfolios 522 in one or more datastructures, where the swap portfolios may include information about twoor more different swaps (e.g., swap 1, swap 2, swap n, etc.). Forexample, the swap information may include a fixed rate value, a floatingrate value, a notional value, and/or a cash value for each of theplurality of different swaps of the swap portfolios 522. A datastructure 523 may be stored corresponding to an adaptive compression ofone or more of the swap portfolio 522, which reducing memory usageand/or bandwidth requirements in communicating data associated with thedata structure 523. In some cases, a compressed data structure 525 maybe reduced to a single data entry, two data entries, or the like. Insome cases, a portfolio may be fully compressed such that all entriescancel, and the uncompressed portfolio may be deleted. The adaptivecompression of the data may be performed based on the data stored in thedata structure(s) being compressed, such as the swap portfolios 522. Insome cases, the swap portfolios 522 may be associated with the financialinstitution, and/or one or more different customers of the financialinstitution. For example, a financial entity and/or a customer of thefinancial entity may desire to enter into one or more different swaps tohedge financial risk due to a plurality of fixed rate holdings and/or aplurality of floating rate holdings. In some cases, a computing device515 and/or the user interface 516 may be used to facilitate user accessto the one or more swap portfolios 522. For example, a user may log intothe financial institution computing system 510 via one or more userinterface screens accessible via the user interface 516. In some cases,the user interface 516 is at a geographical location local to thefinancial institution computer system 510 and/or at a geographicallocation of the user.

In some cases, the clearinghouse computer system 540 may include one ormore of a data repository 542, a computer device 544 and/or a userinterface 546. The clearinghouse computer system 540 may becommunicatively coupled to at least one financial institution computersystem, such as the financial institution computing system 510 via thenetwork 505. In some cases, the clearinghouse computer system 540 may beconfigured to obtain information about one or more of the swapportfolios 522, process the information to blend coupons associated withthe different swaps of the swap portfolios 522 and communicateinformation about the blended swaps to the financial institutioncomputing system 510 to reduce one or more line items associated withthe swap portfolios 522 and/or to reduce a gross notional valueassociated with the swap portfolios 522 to reduce memory requirementswhile additionally reducing a total capital charge incurred by thefinancial institution in relation to the swap portfolios 522.

The present invention has been described herein with reference tospecific exemplary embodiments thereof. It will be apparent to thoseskilled in the art that a person understanding this invention mayconceive of changes or other embodiments or variations, which utilizethe principles of this invention without departing from the broaderspirit and scope of the invention as set forth in the appended claims.

We claim:
 1. A system comprising: a processor and a memory coupledtherewith, the memory storing instructions that when executed by theprocessor cause the processor to: determine that, as data elements areadded to a first data structure which stores a plurality of dataelements, a data size of the data structure exceeds a threshold, each ofthe plurality of data elements including data indicative of a pluralityparameters resulting from an electronic transaction, the plurality ofdata elements collectively being characterized by a third parameter, andbased thereon: calculate a fourth parameter collectively characterizingthe plurality of data elements based on a modification of a first of theplurality of parameters of each of the plurality of data elements as afunction of an associated second of the plurality of parameters;generate, based on the calculated fourth parameter and a selected firstparameter of the plurality of data elements, a value of a firstreplacement data element as a function of a difference there between;generate a value of a second replacement data element when there is adifference between the selected first parameter and the generated valueof the first data element, the value of the second replacement dataelement being based on the difference such that the first and secondreplacement data elements are collectively characterized by the thirdparameter; store the first and, when generated, second replacement dataelements in a second data structure; and use the second data structure,in lieu of the first data structure, for storage in the memory ortransmission, via a communications network coupled with the processor,to another computer system, the storage consuming less of the memory andthe transmission taking less time over the communications network. 2.The system of claim 1, wherein the plurality of data elements comprisesan electronic record of results of a plurality of electronictransactions corresponding to an associated user.
 3. The system of claim2, wherein each data element of the plurality of data elementscorresponds to a result of a different electronic transaction andwherein the first parameter of at least one of the plurality of dataelements is different from the first parameter of another of theplurality of data elements.
 4. The system of claim 3, wherein eachelectronic transaction comprises an interest rate swap.
 5. The system ofclaim 4, wherein the interest rate swap is a Brazilian Real (BRL)interest rate swap (BRL swap).
 6. The system of claim 4, wherein thefirst parameter corresponds to an interest rate and the second parametercorresponds to a notional value.
 7. The system of claim 1, wherein thethird parameter is indicative of the collective cash flow of theplurality of data elements.
 8. The system of claim 1, wherein the firstdata structure is used for storage and the second data structure is usedfor transmission.
 9. The system of claim 1, wherein the first datastructure is one of a plurality of data structures, each storing aplurality of data elements.
 10. A computer implemented methodcomprising: determining, by a processor, that, as data elements areadded to a first data structure which stores a plurality of dataelements, a data size of the data structure exceeds a threshold, each ofthe plurality of data elements including data indicative of a pluralityparameters resulting from an electronic transaction, the plurality ofdata elements collectively being characterized by a third parameter, andbased thereon: calculating, by the processor, a fourth parametercollectively characterizing the plurality of data elements based on amodification of a first of the plurality of parameters of each of theplurality of data elements as a function of an associated second of theplurality of parameters; generating, by the processor based on thecalculated fourth parameter and a selected first parameter of theplurality of data elements, a value of a first replacement data elementas a function of a difference there between; generating, by theprocessor, a value of a second replacement data element when there is adifference between the selected first parameter and the generated valueof the first data element, the value of the second replacement dataelement being based on the difference such that the first and secondreplacement data elements are collectively characterized by the thirdparameter; storing, by the processor, the first and, when generated,second replacement data elements in a second data structure; and using,by the processor, the second data structure, in lieu of the first datastructure, for storage in the memory or transmission, via acommunications network coupled with the processor, to another computersystem, the storage consuming less of the memory and the transmissiontaking less time over the communications network.
 11. The computerimplemented method of claim 10, wherein the plurality of data elementscomprises an electronic record of results of a plurality of electronictransactions corresponding to an associated user.
 12. The computerimplemented method of claim 11, wherein each data element of theplurality of data elements corresponds to a result of a differentelectronic transaction and wherein the first parameter of at least oneof the plurality of data elements is different from the first parameterof another of the plurality of data elements.
 13. The computerimplemented method of claim 12, wherein each electronic transactioncomprises an interest rate swap.
 14. The computer implemented method ofclaim 13, wherein the interest rate swap is a Brazilian Real (BRL)interest rate swap (BRL swap).
 15. The computer implemented method ofclaim 13, wherein the first parameter corresponds to an interest rateand the second parameter corresponds to a notional value.
 16. Thecomputer implemented method of claim 10, wherein the third parameter isindicative of the collective cash flow of the plurality of dataelements.
 17. The computer implemented method of claim 10, wherein thefirst data structure is used for storage and the second data structureis used for transmission.
 18. The computer implemented method of claim10, wherein the first data structure is one of a plurality of datastructures, each storing a plurality of data elements.
 19. A memorymanagement computing device comprising: a processor and a memory coupledtherewith, the memory storing instructions that when executed by theprocessor cause the processor to perform the steps of: determining that,as data elements are added to a first data structure which stores aplurality of data elements, a data size of the data structure exceeds athreshold, each of the plurality of data elements including dataindicative of a plurality parameters resulting from an electronictransaction, the plurality of data elements collectively beingcharacterized by a third parameter, and based thereon: calculating afourth parameter collectively characterizing the plurality of dataelements based on a modification of a first of the plurality ofparameters of each of the plurality of data elements as a function of anassociated second of the plurality of parameters; generating, based onthe calculated fourth parameter and a selected first parameter of theplurality of data elements, a value of a first replacement data elementas a function of a difference there between; generating a value of asecond replacement data element when there is a difference between theselected first parameter and the generated value of the first dataelement, the value of the second replacement data element being based onthe difference such that the first and second replacement data elementsare collectively characterized by the third parameter; storing the firstand, when generated, second replacement data elements in a second datastructure; and using the second data structure, in lieu of the firstdata structure, for storage in the memory or transmission, via acommunications network coupled with the processor, to another computersystem, the storage consuming less of the memory and the transmissiontaking less time over the communications network.
 20. The memorymanagement computing device of claim 19 wherein the memory managementcomputing device uses the first data structure for storage in the memoryand the second data structure for transmission via the communicationsnetwork.